Executive expectations for AI deployment continue to rise, and stakeholders increasingly want clear proof of return on investment. Yet many organizations face a measurement gap: traditional KPIs often fail to capture the full value of AI-enabled service delivery, especially when performance improvements extend beyond speed and efficiency.

In this Customer Engagement Leadership Council session, Pamela Fusting presented a real-world case study of AI deployment in a resident service center, exploring how one early adopter realized the need to re-baseline traditional speed and efficiency KPIs. Instead, a new barometer, Value on Investment (VOI) became a critical benchmark for assessing the deployment’s success.

Key Action Items

• Identify and prioritize high-impact AI use cases within existing service workflows
• Define ROI and VOI metrics that capture efficiency, complexity, cost avoidance, and service quality
• Establish guardrails and operational measures to integrate AI while maintaining service levels

Introduction

Customer Experience leader Pam Fustig introduced herself and briefly described her work at the Tennessee Department of Human Services, which serves 2–2.5M Tennesseans across 95 counties. She outlined the agency’s scope of services and the population served, establishing the contact-center context for the case study.

Pam shared highlights of a 12-month AI deployment process, discussing the initiative and results. She framed the topic as AI deployments in contact centers and referenced industry research showing broad interest in AI and a desire for quick return on investment (ROI).

 Case Study Takeaways

• Timeline: multi-year program with a 12-month AI push
• Abandonment rate spiked from 2.3% to 43.4% initially – a misleading statistic that needed to be put in context
• Average handle time increased after launch – in part because many calls were quickly resolved by automation and the more complex calls that remained required longer handle time
• Containment and self-service obscured failure signals
• Sustained ~25% reduction in after-call wrap-up time
• Insight: AI measurement is a different reality, no standard industry benchmarks currently exist
• Recommendation: shift ROI to a “Value on Investment” metric set – move from a “feature first” mentality to an “outcome based” mindset

KEY DISCUSSION POINTS

Objectives and AI tools

The specific AI tools and objectives deployed over the past year were outlined. They included virtual agents, generative call summarization, authenticated chat, proactive SMS, voice agents, and extended hours for customer service. Routing, agent assist, and auto-summarization were all part of the new configurations. Desired outcomes included faster resolution, improved metrics, and repurposing agents toward casework to demonstrate dollar savings.

Deployment outcomes and metrics paradox

Outcomes after deployment: traditional KPIs moved unfavorably—average handle time rose, abandonment spiked from 2.3% to 43.4%, and inbound volume initially increased—yet much of that reflected call containment where callers completed self-service or received texts and disconnected, prompting a need to re-baseline measurement approaches. A key takeaway: containment must be measured alongside abandonment to avoid misleading conclusions.

Lessons learned, must-dos, and pilot strategy

Pam advocated for measuring “Value on Investment” (financial and non-financial outcomes) and detailed lessons learned: search for performance impact, re-baseline KPIs, establish guardrails and secure executive sponsorship. She recommended prioritizing pilots and building agent capacity to monitor and refine AI. She reported a sustained ~25% reduction in after-call wrap-up time after fine-tuning. She also explained that AI exposes knowledge gaps, shifts caller behavior, and must be examined and/or tweaked before expected ROI is realized. AI tools need months of monitoring and fine-tuning after deployment.

 Q&A and closing

When the floor was opened for questioning, participants affirmed the value-on-investment framing and raised practical questions. When a member asked why early quarters sometimes regress, these initial negative metrics were attributed to a mix of agent onboarding differences and AI behaviors that required more change management, training, and prompt adjustments. A misleading prompt-appeals example was shared –AI language misled (and confused) callers – and the corrective actions needed to remedy it. An important final question was posed: Is AI making it easier for your customers?

The post CASE STUDY: Determining Strategic Priorities and Tech Investments for Measurable ROI appeared first on CustomerContactMindXchange.

A lot of metrics.

NPS. CSAT. Customer Effort. First Contact Resolution. Response time. Service level. Quality. Productivity. And, lest we forget, that one metric that’s been on the dashboard since 2017 and nobody is entirely sure why we still measure it.

We put them on dashboards. We color-code them. We build action plans when they’re red and celebrate when they’re green.

But how often do we stop and ask a much simpler question?

If this metric improved by 20%, would the customer actually notice?

I keep coming back to that question.

Not every metric needs to affect the customer. But if we’re going to call something a customer experience metric, it’s fair to ask what experience it’s actually measuring.

And that’s where things can get uncomfortable. Because sometimes a dashboard can be very green while the experience behind it is still pretty red.

What We Measure Gets Attention

Most metrics probably answered an important question when they first reached the dashboard.

The problem is that metrics have a very long shelf life.

Processes change. Customers change. Technology changes.

The dashboard? Somehow, the dashboard survives.

Over time, we stop asking why we’re measuring something and focus on hitting the target. Dashboards don’t just tell teams how they’re performing. They tell them how to win.

We set a target, track progress, color-code results and recognize success. Maybe there’s even some friendly competition.

We’ve created a game.

If speed is the goal, people get faster. If productivity is the goal, they produce more. People optimize around the score.

They’re doing exactly what we asked them to do.

The problem comes when winning the game becomes disconnected from the customer experience. The metric is green. Meanwhile, the customer is wondering why they had to call three times.

Perhaps the opportunity isn’t to stop gamifying performance. It’s to design a game where winning on the dashboard also means winning for the customer.

Before adding another KPI, ask:

What behavior will this metric encourage?

And then:

Is that behavior going to create a better experience for the customer?

Once we put a number on a dashboard and give it a target, we shouldn’t be surprised when people optimize around it.

We told them it mattered.

The 20% Test

The test isn’t scientific. There isn’t anything magical about 20%. It’s simply enough movement that we should expect something to be different.

The point isn’t the number. It’s the conversation it creates.

Take any metric on your CX dashboard and imagine that tomorrow it improves by 20%.

• Response time drops by 20%.
• Repeat contacts decline by 20%. Even better.
• Productivity improves by 20%.

Now ask:

Would the customer notice?

Sometimes the answer is yes. Sometimes it’s no. But the most interesting answer is:

“I’m not sure.”

If we call something a customer experience metric, we should be able to explain what changes for the customer when that number moves.

If we can’t, we may be asking the metric to do the wrong job.

Instead of immediately asking, “How do we improve this number?” spend a little more time asking, “What is this number supposed to tell us?”

You might be surprised how different the conversation becomes.

Ask the People Who Actually Do the Work

There’s another question worth asking: What’s missing?

Leaders are good at deciding what we want to know. We’re not always as good at knowing what we should know.

We’ve been encouraging employees to challenge our metrics and propose new ones.

That might sound backwards. Usually, leadership creates the scorecard. But the people doing the work have a view of the customer experience most leaders simply don’t.

They know which problems keep coming back, where customers get stuck and which processes require three extra steps for no good reason. They also know which “simple” processes aren’t nearly as simple as the PowerPoint said they were going to be.

That perspective is data.

Our continuous improvement work gives employees ways to question what we’re measuring and point out things leadership may not see.

Some of the best conversations start when someone closest to the work says:

“We’re measuring X, but what we really need to be looking at is Y.”

A handoff. A delay. A repeat contact. An unnecessary approval. A workaround employees have performed for so long that everyone has forgotten it’s a workaround.

Individually, those things can look operational. To the customer, they’re the experience.

Ask the people doing the work. They may already know what your dashboard doesn’t.

Every Metric Needs a Job

Not every metric needs to be something the customer can see or feel. Productivity, cost, compliance, quality and risk matter, even if no customer has ever woken up excited about our internal productivity rate.

The key is understanding each metric’s job. Some measure business or operational health. Some diagnose problems. Others tell us about the customer experience.

Trouble starts when we confuse one for another. A great internal metric can be exactly that: a great internal metric. We don’t need to dress it up as CX to make it important.

So, the next time you review your CX dashboard, pick a metric and, before asking whether it’s green or red, ask:

If this improved by 20%, would our customer notice?

Then ask your team.

You may find the metric is exactly where it belongs, that you’ve been asking it to tell you something it can’t, or that something important is missing altogether.

We don’t need another metric just because we can measure it. And we certainly don’t need another dashboard. Most of us have enough of those.

Because what we measure gets discussed. What gets discussed gets attention. And what gets attention has a pretty good chance of getting better.

So maybe the goal isn’t a better CX dashboard.

Maybe it’s making sure we’re improving the things our customers can actually feel.

Miguel Murillo is a global Customer Experience and service operations leader with more than 20 years of experience transforming organizations through continuous improvement, operating model design and customer-focused execution. He serves as Associate Director, Global Customer Service at Avanos Medical and regularly speaks on CX strategy, operational excellence and leadership.

The post If the Metric Improved 20%, Would the Customer Notice? appeared first on CustomerContactMindXchange.

In this session from our Customer Experience 2026 Executive MindXchange event, participants learned how leading organizations build co-creation operating models that continuously connect customer voice, employee voice, and business context to improve decision-making, accelerate adoption, and deliver measurable business outcomes.

ACTION ITEMS

• Identify a high-priority customer pain point and map customer voice, employee voice, and business context before defining solutions.
• Establish a recurring co-creation mechanism that brings customers, employees, and business stakeholders into decision-making.
• Rapidly test and validate proposed solutions with all stakeholder groups before scaling implementation

 KEY TAKEAWAYS

• We often react quickly but don’t fully fix issues.
• Consider co-creation to help design experiences with customers, employees and stakeholders.
• Use AI to support co-creation, not replace human collaboration.

 STRATEGY

• Shift from episodic feedback to continuous co-creation.
• Leverage long term pilots aimed at decreasing onboarding times.
• Create durable teams that consist of front-line employees and solution architects.
• Leverage incremental improvements for calculated and sustained gains.

Understand the problem

• Go – to the place where work is done.
• See – with humility and fresh eyes.
• Learn – from the front line what is happening.

Co-create with customer reality, business reality, employee reality

Voices within a shared reality might include physician advisory board, medical biller user guide, Voice of the employee, medical billing consultants, product management, product design, business leaders.

IMPLEMENTATION GUIDELINES

Days 1-30: Find the shared reality

• Identify an employee pain point that is already receiving attention.
• Gather customer feedback, employee perspectives, and business context.
• Look for gaps, contradictions, and assumptions between the three.
• Document what each group sees as the actual problem.

Days 31-60: Go, see, learn

• Use co-creation workshops and journey mapping to guide your attention.
• Visit the frontlines, with a sense of curiosity and newness.
• Identify root causes instead of symptoms.
• Measure baseline metrics for future calibration.
• Identify possible solutions

Days 61-90: Prioritize solutions

• Rapidly prototype or pilot a solution.
• Get feedback from customers, employees, and business stakeholders.
• Measure adoption, effort reduction, and customer impact.
• Establish a recurring mechanism to keep realities connected.

FINAL THOUGHTS

Treat customers as active participants in solving problems.

Marina is a product and design leader with two decades of experience helping organizations turn customer and employee insights into measurable business results. She has led customer-centered transformation efforts across healthcare, financial services, logistics, and building technology for organizations ranging from Fortune 20 enterprises to VC-backed startups. Her expertise in co-creation helps companies accelerate innovation, reduce risk, and improve adoption by designing with customers and employees, not just for them.

The post Co-Creating with Customers and Employees to Solve Core Customer Pain Points appeared first on CustomerContactMindXchange.

Key Question

The ultimate goal of every business is to tailor content, offerings, etc., so well that it’s like you’re reading the customer’s mind. And while we are not there yet, technology (AI in particular) is bringing us closer to this reality. But at what point does this become invasive?

Hyper-Personalization

Hyper-personalization was defined as:

• Proactive.
• Predictive.
• Individualized.
• Targeted.
• Contextualized.

Hyper-Personalization Challenges

• Data privacy concerns.
• Resource-intensive data collection.
• Over-personalization.
• Constant maintenance and adaption.
• Potential for algorithmic bias.
• Data accuracy.

Striking a Balance

• Align quantitative + qualitative data.
• AI + people.
• Tools to optimize the information streams.
• Heed pitfalls and recommendations.
• Avoid creepiness.

Quantitative Data: What and How

• A large sample data is the backbone, but quantitative data alone is problematic.
• Quantitative in incremental, so long-fall behaviors can be difficult to categorize.
• Data accuracy can be easily skewed.
• Data focuses on individuals versus cohorts (i.e. relationships).

Qualitative Data: Why

• Individual behavior is shaped by three overarching factors:

1. Psychology: taste, temperament and experience.
2. Language: semantics and semiotics.
3. Culture: shared worldview.

The Promise of AI

Step 1: Remove clutter: Identifying “noise” and feigning causality.
Step 2: Align “outliers” and data through time.
Step 3: Increase opportunities for rapid test and learning.
 

Understanding AI and Context

• While the data may come from multiple sources, AI rarely understands context such as shared interactions, culture, positive outliers, socio-political landscape, demographics, seasonality, time of the day, emotion, religion, location, and more.
• Measure everything – sales productivity, customer outcomes, and operational impact.
• Let the data decide how you lead your hyper-personalization.

Avoid “Creepy Personalization”

• Filter Bubbles: Excessive personalization leads to consumers being trapped in “filter bubbles,” where they only see content that reinforces their existing beliefs and preferences. AI has a tendency to amplify this problem.
• Limited Discovery: Over-personalization can limit customers’ exposure to new ideas and options, hindering their growth and engagement. Satisfaction becomes hedonistic and diminishes experiential highs.
• Trust Erosion: Over-personalization damages customer trust if it feels like the brand is using personal information inappropriately. AI without human intervention can become overly intrusive.
• Over-Saturation: Too many offers can become annoying, leading to other messaging and offers being ignored. Frequency of messaging must align with the frequency of use of the product or service, as well as mental and cultural models of appropriate frequency.
• Mistaken Adjacencies: Just because the system has identified a correlation between data points, thus providing opportunities, be careful about how you act or react.

Action Items

• Identify the pitfalls of overemphasizing one type of data over another.
• Learn how context-based service blueprints can be used to react to behavioral changes.
• Identify how AI can speed up targeting opportunities.
• Learn how to avoid going from helpful to creepy with your personalization strategy. 

Final Thoughts

• Data is not enough.
• AI will streamline the research process but cannot replace it.
• Seek behavior change through time, not reactionary impulses.
• Aligning user context with your business needs is more important than ever.

The post Hyper-Personalization: Crafting Tailored Experiences for the Modern Consumer appeared first on CustomerContactMindXchange.

No company sets out to create customers for a single transaction or moment. Ask any leadership team what it wants and a familiar answer emerges: customers who stay, buy more, advocate and choose the company again. It is one of the few objectives that everyone in an organization can agree on. Yet that consensus creates a blind spot. Because although everyone agrees that loyalty matters, few stop to ask whether the enterprise is actually designed to produce it.

In the book I recently co-authored, The Customer Excellence Enterprise: A Playbook for Creating Customers for Life, that aspiration is the starting point. Companies design brands with rigor, engineer products with discipline and build sophisticated commercial plans around both. However, the experience connecting them is often left to functions, processes and individuals expected to make the whole thing work.

When it does not, the relationship is usually rescued by the person standing closest to the customer: the sales representative, account manager, or contact center agent apologizing for a policy they did not create and working around a process they do not have authority to fix. Organizations celebrate these heroic acts as exceptional service when they may really be evidence of a systemic failure.

These failures are rarely episodic. They typically reflect unclear ownership, disconnected incentives, rigid silos, leadership gaps and processes optimized around internal efficiency rather than customer outcomes. Meanwhile, customers are learning to route themselves around the enterprise. They search with AI, consult communities and follow independent voices who have already documented where a company disappointed them.

The companies most likely to create customers for life will stop treating loyalty, advocacy, and repeat purchases as an outcome to hope for and start treating it as an architectural challenge. They can start that journey with two foundational ideas and three bold enterprise moves Tom DeWitt and I discuss in our book:

Two Foundational Ideas

1. The organization must be predisposed before the customer arrives

Predisposition is the architectural idea at the center of The Customer Excellence Enterprise: A Playbook for Creating Customers for Life. The book examines whether the leadership, organizational, operational and commercial conditions for a good customer outcome exist before the interaction begins. In a predisposed organization, ownership is clear, standards are understood, information travels with the customer, work crosses boundaries without repeated negotiation, and frontline employees have enough context and authority to resolve issues where they arise.

This shifts the burden of customer-centricity from individual heroics to organizational design. The more customer outcomes depend on exceptional employees compensating for the enterprise around them, the less customer centric that enterprise is by design. It is also why AI will not rescue poor architecture. AI can increase speed, intelligence and responsiveness, but it learns from and scales the system into which it is introduced, including its fragmentation, waste, and lack of institutional empathy.

2. Helpfulness must become the organizing principle

Every enterprise needs a principle capable of connecting strategy, operations and experience. Helpfulness works because customers understand it immediately and employees recognize it instinctively. Making helpfulness the throughline means asking a different question of every interaction, decision and capability: does this help the customer reach the outcome they are trying to achieve?

“The Customer Excellence Enterprise leans on the timelessness and elegant simplicity of helpfulness, a quality inherent to the human condition, alive in every one of us, and by extension present in the potential of every company and how they interact with, treat and value their customers.”

That standard is different from asking whether an interaction advances someone through our funnel, increases engagement or satisfies a channel objective. It reframes the enterprise around customer progress rather than internal activity.

The arena has also changed. If customers are troubleshooting a product on Reddit, using an AI-generated answer, consulting a peer community or watching an independent expert, helpfulness cannot exist only inside channels the company owns. Knowledge becomes commercial infrastructure, making expertise discoverable and useful where customers actually seek help.

Three Bold Enterprise Moves

1. Mitigate pain points at the root cause

As discussed in our book, Operations Bold Move #10 calls for deeper examination of customer pain points rather than the common response of routing around them. Lean thinking offers a useful lens: Muda, Mura and Muri. Muda is waste, the steps and activity that add no customer value. Mura is unevenness, the inconsistency that makes an experience depend on channel, employee or circumstance. Muri is overburden, excessive effort placed on customers or employees by poor system design.

Together, they reveal why friction is rarely just a “pain point.” A repeated request may look like an isolated irritation but reflect waste in the process, inconsistency between functions or overburden transferred to the customer. A workaround preserves the architecture that produced the problem. Root cause mitigation removes it and creates room for differentiated value to be noticed. Customers cannot appreciate what makes a company exceptional while navigating complexity the company has chosen to tolerate.

2. Re-align the enterprise around customer lifetime value

Organizational Bold Move #10 describes customer lifetime value as the house guest that never leaves: always present, frequently discussed, yet rarely as prominent as immediate financial measures. That exposes a fundamental tension. Companies speak the language of long-term relationships while planning cycles and incentives privilege short term outcomes.

A company cannot credibly pursue customers for life while indexing every important decision by the quarter. Customer lifetime value has to move from conceptual importance to structural reality. Put it alongside the projections, investment choices and performance measures that shape executive behavior, and different decisions about service, retention, experience and customer investment become possible.

3. Make Customer Excellence a board level issue

Leadership Bold Move #6 provides the governance necessary to sustain the other four. Boards already oversee systemic issues that affect enterprise value, and Customer Excellence belongs in that conversation because experience ultimately shows up in four areas boards understand well: revenue, reputation, risk and returns, the Four Rs.

The practical question is simple: which committee owns the gap between the experience the company promises and the experience customers actually receive? If the answer is unclear, Customer Excellence remains an intention rather than an enterprise capability, and intentions are vulnerable when budgets tighten, leaders change or short-term performance comes under pressure.

None of these moves depend on delighting customers at every interaction. Delight is episodic. What compounds is an enterprise designed to be helpful, one that removes structural reasons for customers to leave, values the relationship beyond the immediate transaction and places accountability high enough for the work to endure.

Wayne Simmons is the founder of The Customer Excellence AGENCY and a customer experience thought leader and speaker. His career spans CX leadership at Pfizer, Bayer, and The Ritz-Carlton Leadership Center. He is also a founding faculty member of the Master of Science in Customer Experience Management program at Michigan State University’s Broad College of Business, where he works with leadership teams to embed experience as a value driver and build durable competitive advantage.

The Customer Excellence Enterprise is available on Amazon and anywhere books are sold.

The post “Customer for Life” Is an Architecture, Not an Ambition appeared first on CustomerContactMindXchange.

For years, customer experience and cybersecurity were kept apart. The experience teams cared about speed and convenience. The security teams cared about protecting systems, identities and data. AI is pulling those two worlds together. The more an AI system can do, the more we need to know what it can reach, what it can decide and what it is allowed to do.

Customer Experience Is Becoming More Autonomous

Old style customer service waits for a problem. The customer runs into trouble, gets in touch and waits for help. AI makes something very different possible.

A bank might spot an odd activity and warn the customer before fraud spreads. An airline might see that a delay will break a connection and offer another flight. A phone company might notice weak service and fix it before anyone calls. These moments save the customer effort, and over time they build loyalty.

The next step goes further. AI assistants are starting to do tasks, not just answer questions. A customer might ask a banking assistant to check recent payments and find repeating subscriptions, then ask to cancel one or challenge a charge they do not recognize. Now the AI is not just giving information. It is taking action. From a security point of view, that changes everything.

Identity Becomes a Core Control

When someone logs into a company system, we want to know who they are and what they are allowed to do. AI adds one more layer. Now we also have to know which AI is acting, whose permission it is using, what it can see and how long that access should last.

An AI assistant should not get every permission the person using it has. If a customer asks it to look at their account, it may need short term permission to read their transactions. If that customer then asks it to move money or change personal details, the system should require a higher level of approval.

This is where (the concept of?) least privilege matters. Give the AI only the access it needs for one job and take that access away once the job is done. As companies add more AI agents and connect them to more systems, this becomes a must, not a nice to have.

Personalization Has a Security Cost

Companies want AI to know their customers well, because better context usually means a better experience. That requires data. An AI system might pull in past payments, earlier chats, preferences, browsing history and service records to make an interaction feel personal.

For the customer experience, that is useful. For security, it means a lot of sensitive information gathered in one place. So, the question is not only whether the data is there. It is whether the AI truly needs that information to do what the customer asked. More data does not always mean a better experience. Often the safer and more responsible choice is to use only what the task requires.

AI Creates New Attack Paths

Generative AI also brings risks that older software was never built for. Prompt injection is the clearest one. These systems read customer messages, documents, web pages and internal knowledge bases, and someone can hide harmful instructions inside that content to change what the AI does. The risk grows the moment the AI can use tools or take action.

Companies need a firm line between what the AI is allowed to read and what it is allowed to obey. And security cannot stop at the model itself. The Application Programming Interfaces (APIs) it calls, the systems it links to, the credentials it holds and the data it pulls in are all part of what can be attacked.

This is why AI driven customer experience cannot be one team’s job. Cybersecurity, identity, privacy, risk, data governance and customer experience all need to be involved from the start.

Governance Can Enable Innovation

Governance is often seen as the thing that slows new ideas down. In real life it does the opposite. When a company knows which AI took an action, which customer allowed it, what data it used and what controls were in place, it can grow those tools with far more confidence.

Being able to audit matters most here. For any important interaction, a company should be able to piece together what happened. Who started the request. Which AI handled it. What information it saw. What permission it was given. What it actually did. When a customer complains, “the AI decided” cannot be the answer. A person still has to be accountable.

Trust Will Be the Differentiator

AI will keep getting easier to use. Soon most large companies will offer smart assistants and AI powered automation, so simply having AI will not make anyone stand out. Trust will.

Customers may never see how an assistant is built, but they can feel whether the experience is safe, accurate and respectful. The companies that lead in AI driven customer experience will not be the ones that give AI the most freedom. They will be the ones that know how much freedom to give it, and can show that the right identity, security and governance controls are already in place.

AI can make customer experiences faster and smarter. Security and governance are what will allow a company to grow those experiences without losing the thing that matters most, which is customer trust.

Mrunal Gangrade is a Vice President of Engineering specializing in AI, explainable AI, and cybersecurity governance in regulated industries. An IEEE-published researcher and Gold Winner at the Women in Tech Global Awards 2025, she has spoken at events across multiple countries on building trustworthy, transparent AI systems that deliver measurable business value.

The post From Personalization to Autonomous Action: Securing AI Driven Customer Experience appeared first on CustomerContactMindXchange.

Your next customer may never touch the journey you spent months improving. They may not visit your homepage, open your app, read your comparison page, scroll through your FAQ, or follow the flow your team fought to simplify. They may ask an AI agent to do the work for them.

“Find the best internet plan for my home.”
“Compare these insurance options.”
“Check whether I can return this and tell me what to do next.”

That’s useful for the customer and a little uncomfortable for CX.

For years, we’ve treated the owned journey as the center of customer experience: the website, app, chatbot, contact center, store, and account portal. That made sense when customers were doing the work themselves. AI agents change the front door. The customer may still make the final decision, but the research, comparison, filtering, scheduling, and preparation may happen somewhere your team doesn’t control.

That means the work underneath the journey starts to matter more than the journey itself: product truth, policy clarity, knowledge quality, recovery paths, handoffs, and permission rules. All the things customers usually have to fight through quietly will move much closer to the surface.

When the Customer Isn’t the One Clicking
Most CX work assumes the customer is present. We map what customers see, measure where they click, study where they abandon, improve the form, rewrite the help article, reduce the steps, and celebrate when completion improves. That work still matters. It just may not be where the decision starts.

When customers delegate a task to an AI agent, the experience becomes whatever the agent can understand, retrieve, compare, trust, and act on. The agent doesn’t care that your homepage feels warm and aspirational. It won’t admire the photography or follow your product story in the order marketing intended. It wants the truth.

What does this cost? Who is eligible? What’s included? What happens if I cancel? Where do I go when something breaks? If those answers are scattered, stale, inconsistent, or buried in a PDF from three product launches ago, the experience is already fragile. The customer may never know where it broke. They’ll only know the answer was wrong, the recommendation was confusing, or the next step created more work than it removed.

Then the company gets the call.

The Customer Has Been Doing the Cleanup
A human customer can work around messy systems. They can open five tabs, read three help pages, call support, compare notes with a friend, study the fine print, and make an educated guess. That doesn’t mean the experience worked. It means the customer did the cleanup.

An AI agent may expose the mess faster. It might pull from your policy page, a support article, a comparison site, a customer review, and an old product document. Then it may summarize the answer as if your company has one clear version of reality. Many don’t.

Take home internet. A customer asks an agent to compare providers based on speed, price, installation timing, equipment fees, promotional expiration, contract terms, and cancellation rules. The agent won’t move through the journey the way your UX team designed it. It’ll assemble the decision from whatever it can find and trust. One provider has the nicer website but vague fee language. Another has less polish but clear pricing, structured plan information, visible installation windows, and cancellation terms that don’t require a scavenger hunt.

The agent may favor the company with cleaner truth over the company with cleaner visuals.

Companies have spent years improving the surface of the experience while the operating layer stayed messy underneath. Product information conflicts. Policy pages fall out of date. Exceptions live in employees’ heads. Recovery depends on finding the one person who knows what to do.

AI doesn’t make those problems disappear. It moves them closer to the customer.

Start With One Real Customer Task
The answer isn’t to chase every new AI interface or try to control every outside recommendation. Start with one high-value customer task: buying a plan, filing a claim, changing service, returning a product, booking an appointment, or renewing a contract. Review the actual sources an agent could use, including product pages, help articles, policy documents, public reviews, comparison sites, internal knowledge, and customer communications. Mark every place where the agent would have to guess, reconcile conflicting information, or send the customer somewhere else.

Then check these three things:

1. Can the agent find the correct answer without guessing?
2. Can it compare prices, fees, eligibility rules, exclusions, timelines, and tradeoffs clearly?
3. Can it tell which source to trust when the website, chatbot, support team, and policy documents don’t agree?

The customer also needs to understand what the agent is about to do, what access they’re providing, and where approval is required. If the agent makes the wrong move, there must be a practical way to reverse it without three transfers, competing policy interpretations, and a ticket number nobody can find. Task completion alone is a weak standard. The customer should also be able to understand, approve, and recover from what the agent did.

The Seams Are Moving
I don’t think CX has lost control of the experience. I think the idea of control was always a little inflated.

Customers have always built their own version of the journey. They asked friends, searched forums, read reviews, compared competitors, and called twice to see if two employees gave the same answer. They took screenshots because they didn’t fully trust the company to remember what it promised. AI agents make that behavior faster and reduce the amount of work the customer has to do personally. The customer is still trying to accomplish something. The company is still responsible for whether its promise holds up. CX still has to see across the seams.

But the seams are moving. They now sit between AI agents, data sources, external summaries, permission models, internal policies, operating systems, and the customer’s final moment of trust. If your experience only works when a patient customer clicks through the right screens, reads everything in the intended order, catches the exception, understands the fine print, and contacts the correct department when something breaks, you may not have a dependable customer experience.

You may have a dependency on customer labor. AI agents will expose that quickly. CX leaders now have to determine what an agent needs to represent the company accurately, help the customer act with confidence, and recover when something goes wrong. AI may change where the journey begins. The company still owns whether its promise holds up.

Mark Levy is a customer experience and product executive, author, and advisor with more than 25 years of operating experience at Fortune 500 companies. He writes Decoding Customer Experience and is the author of The Psychology of CX 101. Connect with him on LinkedIn.

The post Your Next Customer May Not Visit Your Website appeared first on CustomerContactMindXchange.

Known as The Global Customer Experience Connoisseur, Natalie Beckerman is an internationally recognized titan in the world of customer experience.

Her new book, When Did You Stop Caring?  offers a seasoned analysis of customer service strategy triumphs and failures, as delivered by organizations large and small. Throughout, she refutes the lie that “caring” is soft and instead explains how when done right, represents a disciplined, profit-driven approach to customer service. In myriad ways, Natalie reminds leaders that they do business with people, not spreadsheets.

More importantly, she paves the way forward, outlining how to deliver better customer service outcomes. Discussing lived use cases from her own life and expansive career, she illustrates how empathy and efficiency can go hand in hand.

Diving deeper, Frost & Sullivan, The Transformational Growth Company, ® recently posed the following questions about When Did You Stop Caring? to Natalie. Read on for her insightful responses.

The Caring Recession

Frost & Sullivan:  Your new book, When Did You Stop Caring,? opens with a discussion about the “caring recession,” a key reason you wrote the book. Can you briefly discuss this concept and why addressing it is critical to better customer service?

Natalie Beckerman: No one just wakes up one day and stops caring. We’re experiencing a Caring Recession. We have moved way past what we used to refer to as a crisis. The Caring Recession is not some poor quarterly performance or simply a rude employee – it’s a sustained structural withdrawal of human care from organizations at scale.  You can recover from a crisis; a recession reshapes expectations forever. I called it a recession on purpose, because, like an economic one, it compounds overnight before anyone names it. Customer Service is where it’s felt first and worst because that is the front door of the whole enterprise.  Unless we give it a name, then we won’t be able to start reversing its effects. By naming it we can begin the journey to bring the human lens back in balance – and we can start to care on purpose again.

Culture and the Middle Layer

Q:  In Part 2, you discuss how important culture is to a company’s success. You zero in on the importance of the middle layer. Can you elaborate? Any insights to help middle managers caught between C-Suite directives and challenged customer service teams?

A: Culture is not created by the executive level, nor is it lived at the top. It is passed through the middle. The C-suite creates the policy, but middle management will determine whether the policy is actually implemented. Therefore, I always call culture a base or foundation, not a sign or a poster. Middle Management sits on the fault line: executives sending orders down from the top, overworked staff members looking up, and everyone assumes middle management will simply take the heat off. We need to stop using middle management as a “relaying” point where pressure flows downward from upper management and begin viewing middle management as the ones who can provide air coverage to do the right thing.  A successful middle manager translates for their team, determines how policy affects the customer in front of him or her, and provides support so their employees can make the correct decisions.

Operationalizing the Mission

Q:  Operationalizing the enterprise mission statement (rather than just messaging around it) through the actual policies, procedures and actions of employees is another key concept in your book. How do you put this into practice? Any guidelines on how to make your company’s philosophy a lived reality for customers?

A: The space between a business’s claimed beliefs and the real-life experience of a customer is called the “Caring Gap.” Typically, the Caring Gap is filled with good intention and poor policy. An internal statement regarding a company’s belief system or their mission communicated as a message to customers is an example of a mission statement; however, when that same mission statement is embedded into a company’s operational practices, it becomes an example of a mission being lived.

To close the Caring Gap, I suggest that company leaders review every policy they have created and ask themselves if that particular policy supports their employees’ ability to provide better service to customers. Does it create barriers for their employees? Next, identify the times when team members are forced to make a decision regarding whether they should follow procedure, or do what is right for your customer at that moment, because during those moments your team members are testing your stated mission and discovering what you truly believe. Provide your team with the authority to use their own discretion in making decisions for customers. This is the Human Standard. It is not a marketing slogan that you can merely announce; rather, it is a measurement standard that you must live up to in all your operational activities.

The Business Case for Caring

Q:  In Part 4 you present the “business case for caring” — why is this important? How does caring affect return on investment? How does automation factor in?

A: Caring is not soft. It is economics. I wish more finance teams understood that. Loyal customers are cheaper to retain than to get back, they spend greater money over time and forgive you when you stumble – those are return on investment (ROI) statistics and sentiment.

Sheila (the name of my car) that was returned, made me a loyal customer of a brand for years because that brand didn’t argue with me about fixing my issue. Instead, they gave me their standard service which went the extra mile.

The turnover you avoid when your employees feel valued is ROI too. Every person who stays is one hire you didn’t have to make twice.

Automation is not the villain here. It’s indifference. Used properly, automation removes friction and drudgery so your people can be free for the moments which truly require a human — complicated, emotional, or the kind that people will remember. The failure is when we remove these very moments from our people by using automation to save small amounts of money.

Care is what compounds. Everything else is just cost management disguised as strategy.

Efficient versus Built to Last

Q:  Can you distinguish between a business that is very efficient/profitable versus one that is built to last? How does the organizational mindset differ? Any practical applications you can point to?

A: Efficiency is an important goal for companies; however, there is a difference between efficiency and being “built to last.” When organizations focus solely on being the most efficient, they may achieve short-term goals such as low costs or high profit margins. However, by focusing solely on how to lower their costs (i.e., eliminate non-essential jobs) while maintaining productivity, these organizations will ultimately fail.

Companies that have long-term goals tend to focus on building strong relationships with their customers, and therefore, the quality of service will be higher. Additionally, employees who work for companies that build long-lasting customer relationships are often rewarded, which also creates employee loyalty. As a result, employees become the greatest asset of a company. Trust also becomes an important component of a successful business. Building trust takes time and effort, but when achieved, it helps create long-lasting relationships with both employers and customers. Therefore, businesses that treat employees as assets and view trust as capital will be able to develop sustainable growth.

The Beckerman Caring Challenge™

Q:  Your book closes with “the Beckerman Caring Challenge” — a 12-month commitment to restore and reignite humanity. No small feat! Can you share a few key action steps with our readers?

A: A twelve-month plan seems overwhelming at first. The Beckerman Caring Challenge™ is a 12-month, 4-quarter roadmap to take your beliefs and transform them into tangible and observable leadership behaviors by putting your values into action.

You can begin wherever you’d like within the four quarters of the Challenge. Just get started. The Challenge was designed to create habits. Here are a couple of examples of the foundational elements I would consider starting with:

First, complete an objective assessment — identify your Caring Gap before announcing a single new project or program. You cannot address what you’re unwilling to acknowledge.

Second, establish a handful of commitments that will be non-negotiable, and then display them as such. Good intentions have a low success rate when it comes to actual completion. What holds people accountable to their commitments is “caring out loud.”

Third, schedule accountability on the calendar once each month — one intentional opportunity per month for leaders to ask themselves, “In which areas did we demonstrate caring? In which areas did we fail to care? What are we going to change?”

Accountability is what differentiates a value from simply being a feeling or vibe. And running underneath all of it is what I call the Flame Forward: the idea that reignited humanity isn’t something you keep, it’s something you pass on.  Do this for a year and you don’t just improve your metrics; you change what kind of company you are. And you bring the lenses of humanity and efficiency back in balance and back together.

Don’t Miss Natalie at Customer Contact West

Natalie Beckerman will be hosting a CX Women’s Leadership Alliance Workshop at the 22^nd Annual Customer Contact West: A Frost & Sullivan Executive MIndXchange in Tucson, Arizona from October 18^th to 21^s.t.  The event will take place at the JW Marriott Tucson Starr Pass Resort & Spa and the Women’s Workshop, themed Owning Your Growth Journey, will take place on Sunday, October 18^th at 2:15pm. Learn more here.

Described as a fixer in the customer experience and customer contact space, Natalie Beckerman has run top tier, large scale operations driving transformation and change, leading strategy, while conquering operational and service excellence across the Globe. She is known to build new business, increase revenue and turn around performance, while at the same time reenergizing and invigorating businesses and leaders. Natalie has done this in the telecommunications/mobile, Home Services, Hospitality and Fintech/Financial services industries.

The post When Did You Stop Caring? The Call to Reignite Humanity in a World Obsessed with Efficiency appeared first on CustomerContactMindXchange.

The real strategic advantage comes from transforming data into actionable insights that inform better decisions, build trust, and enable sustainable growth. Enterprise Customer Intelligence™ provides a framework for organizations to leverage customer understanding as a key strategic asset – positioning them ahead of competitors to secure long-term success.

This creates a paradox: despite abundant customer information, enterprise capabilities often remain fragmented. Insights are generated across various functions but are rarely integrated into a unified model that supports decision-making, governance, and trust.

The Need for Enterprise Customer Intelligence

Organizations need to move beyond isolated feedback programs and siloed data. The goal is to develop a comprehensive, real-time, integrated, and actionable understanding of customers. The Model provides a framework to achieve this by linking customer insights, decision-making, and governance to improve organizational performance and foster customer trust.

For CX and contact center leaders, the practical test is whether customer intelligence is being used to improve service design, reduce friction, and make faster, more informed decisions during moments that matter.

The Enterprise Customer Intelligence Transformation Model

This model converts customer signals into improved enterprise decisions. It underscores that customer trust and relationship health are vital indicators of whether these decisions strengthen relationships and create lasting value.

The model is built on four interconnected capabilities that progressively turn customer understanding into enterprise-wide intelligence, stronger trust, and sustainable success:

1. Build Unified Customer Understanding

Achieving a unified view of the customer requires transcending isolated surveys and feedback. Integrating Voice of Customer data, behavioral analytics, operational data, journey insights, and Customer 360 information provides a real-time, holistic picture of customer needs, expectations, and emerging friction points.

2. Align Intelligence with AI and Decision-Making

Customer intelligence adds value when it directly informs decisions. AI enhances this by processing large data volumes, identifying patterns, and generating predictive insights. Leading organizations leverage AI not only for customer service automation but also for strategic planning, risk detection, investment prioritization, and performance management. The strategic advantage lies in translating insights into actionable improvements.

3. Operationalize Intelligence Through Better Decisions

Embedding customer insights into decision processes transforms customer intelligence into an enterprise capability. Decision intelligence integrates understanding into functions, such as finance, operations, product development, marketing, customer service, and strategic planning. This approach enables organizations to evaluate trade-offs more effectively, prioritize initiatives, reduce friction, and align actions with customer needs and business objectives.

4. Strengthen Trust Through Governance

As AI and predictive models become central to operations, robust governance is essential. Trust-centered governance requires clear standards for transparency, privacy, ethics, and responsible AI. Demonstrating responsible data management and model use fosters customer trust and aligns organizational practices with ethical standards and customer expectations.

From Insight to Enterprise Value

To realize true value, customer intelligence must extend beyond insights teams and marketing functions. It must influence decision-making across the C-suite and throughout the organization. When integrated – combining understanding, AI, decision intelligence, and governance – organizations can:

• Reduce Enterprise Risk: Better identify friction points, compliance issues, emerging risks, and vulnerabilities early, reducing operational disruptions and reputational damage.
• Improve Enterprise Performance: Use customer insights to allocate resources more effectively, enhance efficiency, and prioritize high-impact initiatives.
• Strengthen Long-Term Value: Protect customer data, communicate transparently, and deliver experiences aligned with customer expectations – building loyalty, increasing lifetime value, and enhancing resilience and competitive positioning.

Strategic Implications

The next competitive advantage will not rely solely on data volume or AI sophistication. Instead, it depends on an organization’s ability to translate customer understanding into decisions that drive measurable outcomes. Enterprise Customer Intelligence – by integrating insight, AI, governance, and decision-making – shifts the focus from merely understanding customers to actively enhancing decisions, building trust, and creating long-term value.

For executive teams, the critical question is not how much data is available, but whether customer insights influence decisions that strengthen trust and relationships over time. Organizations that excel at this will differentiate themselves through stronger execution, more responsive decision-making, and enduring customer relationships.

By adopting Enterprise Customer Intelligence as a strategic capability, organizations can harness the full potential of their customer data – driving innovation, resilience, and leadership in their markets. Prioritizing this transformative methodology becomes essential for organizations seeking sustained competitive viability.

John Bord is a seasoned customer strategy executive, consultant, and thought leader with over 25 years of experience in Customer Experience (CX), Customer Intelligence, Voice of the Customer (VoC), strategic marketing and enterprise transformation. A Forrester Certified Customer Experience Professional, he is the author of the Enterprise Customer Intelligence™ Transformation Model and the Customer Trust & Relationship Health™ framework, which explore how organizations turn customer understanding into better decisions, stronger relationships, and sustainable enterprise value.

The post Unlocking Competitive Advantage: The Strategic Power of Enterprise Customer Intelligence appeared first on CustomerContactMindXchange.

Presented by DanaShapir Alviene, Chief Guest Officer, Breeze Airways

The post HEADLINER VIDEO: AI Without Fear: Building Trust in a Fast-Changing World appeared first on CustomerContactMindXchange.